import os
os.chdir("../data/raw")
os.getcwd()

'/root/restaurants/data/raw'

import zipfile
with zipfile.ZipFile("dataset.zip", 'r') as zip_ref:
    zip_ref.extractall("")

with zipfile.ZipFile("air_reserve.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")
with zipfile.ZipFile("hpg_reserve.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")

with zipfile.ZipFile("air_visit_data.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")
with zipfile.ZipFile("air_store_info.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")
with zipfile.ZipFile("hpg_store_info.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")

with zipfile.ZipFile("date_info.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")

with zipfile.ZipFile("store_id_relation.csv.zip", 'r') as zip_ref:
    zip_ref.extractall("")

import pandas as pd
import numpy as np
from ydata_profiling import ProfileReport
import seaborn as sns
import matplotlib.pyplot as plt
from copy import deepcopy
import random

import plotly.graph_objects as go
import plotly.express as px

air_reserve = pd.read_csv("air_reserve.csv")
profile = ProfileReport(air_reserve, title="Profiling Report")
profile

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air_reserve.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 92378 entries, 0 to 92377
Data columns (total 4 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   air_store_id      92378 non-null  object
 1   visit_datetime    92378 non-null  object
 2   reserve_datetime  92378 non-null  object
 3   reserve_visitors  92378 non-null  int64 
dtypes: int64(1), object(3)
memory usage: 2.8+ MB

air_reserve['visit_datetime'] = pd.to_datetime(air_reserve['visit_datetime'])
air_reserve['reserve_datetime'] = pd.to_datetime(air_reserve['reserve_datetime'])

hpg_reserve = pd.read_csv("hpg_reserve.csv")
profile = ProfileReport(hpg_reserve, title="Profiling Report")
profile

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hpg_reserve.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2000320 entries, 0 to 2000319
Data columns (total 4 columns):
 #   Column            Dtype 
---  ------            ----- 
 0   hpg_store_id      object
 1   visit_datetime    object
 2   reserve_datetime  object
 3   reserve_visitors  int64 
dtypes: int64(1), object(3)
memory usage: 61.0+ MB

hpg_reserve['visit_datetime'] = pd.to_datetime(hpg_reserve['visit_datetime'])
hpg_reserve['reserve_datetime'] = pd.to_datetime(hpg_reserve['reserve_datetime'])

hpg_reserve = hpg_reserve.rename(columns={"hpg_store_id": "store_id"})
air_reserve = air_reserve.rename(columns={"air_store_id": "store_id"})
reserve_data = pd.concat([air_reserve, hpg_reserve])

reserve_data

hpg_visit_data = hpg_reserve.drop(columns = ['reserve_datetime']).groupby(by = ["store_id", "visit_datetime"]).sum().reset_index()
hpg_visit_data = hpg_visit_data.rename(columns={"visit_datetime": "visit_date", "reserve_visitors": "visitors"})
profile = ProfileReport(hpg_visit_data, title="Profiling Report")
profile

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hpg_visit_data['visit_date'] = pd.to_datetime(hpg_visit_data['visit_date'])
hpg_visit_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1711380 entries, 0 to 1711379
Data columns (total 3 columns):
 #   Column      Dtype         
---  ------      -----         
 0   store_id    object        
 1   visit_date  datetime64[ns]
 2   visitors    int64         
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 39.2+ MB

air_visit_data = pd.read_csv("air_visit_data.csv")
profile = ProfileReport(air_visit_data, title="Profiling Report")
profile

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air_visit_data['visit_date'] = pd.to_datetime(air_visit_data['visit_date'])
air_visit_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 252108 entries, 0 to 252107
Data columns (total 3 columns):
 #   Column        Non-Null Count   Dtype         
---  ------        --------------   -----         
 0   air_store_id  252108 non-null  object        
 1   visit_date    252108 non-null  datetime64[ns]
 2   visitors      252108 non-null  int64         
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 5.8+ MB

hpg_visit_data = hpg_visit_data.rename(columns={"hpg_store_id": "store_id"})
air_visit_data = air_visit_data.rename(columns={"air_store_id": "store_id"})
visit_data = pd.concat([air_visit_data, hpg_visit_data])
visit_data['visit_date'] = visit_data['visit_date'].dt.date
visit_data

air_store_info = pd.read_csv("air_store_info.csv")
profile = ProfileReport(air_store_info, title="Profiling Report")
profile

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hpg_store_info = pd.read_csv("hpg_store_info.csv")
profile = ProfileReport(hpg_store_info, title="Profiling Report")
profile

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hpg_store_info = hpg_store_info.rename(columns={"hpg_store_id": "store_id", "hpg_genre_name": "genre_name", "hpg_area_name": "area_name"})
air_store_info = air_store_info.rename(columns={"air_store_id": "store_id", "air_genre_name": "genre_name", "air_area_name": "area_name"})

store_info = pd.concat([air_store_info, hpg_store_info])
store_info

date_info = pd.read_csv("date_info.csv")
date_info

date_info.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 517 entries, 0 to 516
Data columns (total 3 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   calendar_date  517 non-null    object
 1   day_of_week    517 non-null    object
 2   holiday_flg    517 non-null    int64 
dtypes: int64(1), object(2)
memory usage: 12.2+ KB

visit_data

visit_data

visit_data = visit_data.rename(columns={"visit_date": "date"})
visit_data['date'] = visit_data['date'].astype("string")

date_info = date_info.rename(columns={"calendar_date": "date"})
date_info['date'] = date_info['date'].astype("string")

data = pd.merge(visit_data, date_info, on="date")
data = pd.merge(data, store_info, on="store_id")
data['date'] = pd.to_datetime(data['date'])
profile = ProfileReport(data, title="Profiling Report")
profile
data

temp = deepcopy(data)
temp = temp.groupby(by=['holiday_flg']).count().reset_index().drop(columns=["date", "visitors", "day_of_week", "genre_name", "area_name", "latitude", "longitude"])
temp = temp.rename(columns={"store_id": "count"})

fig = px.bar(temp, x="holiday_flg", y="count",
             barmode='group',
             height=400)
fig.show()

temp = deepcopy(data)
temp = temp.drop(columns=["store_id", "date", "holiday_flg", "genre_name", "area_name", "latitude", "longitude"])
temp = temp.groupby('day_of_week').sum().reset_index().sort_values("visitors")

fig = px.bar(temp, x='day_of_week', y='visitors',
             color='visitors',
             labels={'pop':'population of Canada'}, height=400)
fig.show()

store_id_relation = pd.read_csv("store_id_relation.csv")
store_id_relation

air_store_info = air_store_info.rename(columns={"store_id": "air_store_id", "genre_name": "air_genre_name", "area_name": "air_area_name", "latitude": "air_latitude", "longitude": "air_longitude"})
hpg_store_info = hpg_store_info.rename(columns={"store_id": "hpg_store_id", "genre_name": "hpg_genre_name", "area_name": "hpg_area_name", "latitude": "hpg_latitude", "longitude": "hpg_longitude"})

store_relation = pd.merge(store_id_relation, air_store_info, on="air_store_id", how="left")
store_relation = pd.merge(store_relation, hpg_store_info, on="hpg_store_id", how="left")

store_relation.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   air_store_id    150 non-null    object 
 1   hpg_store_id    150 non-null    object 
 2   air_genre_name  150 non-null    object 
 3   air_area_name   150 non-null    object 
 4   air_latitude    150 non-null    float64
 5   air_longitude   150 non-null    float64
 6   hpg_genre_name  63 non-null     object 
 7   hpg_area_name   63 non-null     object 
 8   hpg_latitude    63 non-null     float64
 9   hpg_longitude   63 non-null     float64
dtypes: float64(4), object(6)
memory usage: 11.8+ KB

sns.heatmap(store_relation.isna(), yticklabels=False, cbar=False)

<Axes: >

store_relation = pd.read_csv("store_id_relation.csv")

store_relation = pd.merge(store_relation, air_store_info, on="air_store_id", how="left")
store_relation = pd.merge(store_relation, hpg_store_info, on="hpg_store_id")

air_store_info = air_store_info.rename(columns={"air_store_id": "store_id", "air_genre_name": "genre_name", "air_area_name": "area_name", "air_latitude": "latitude", "air_longitude": "longitude"})
hpg_store_info = hpg_store_info.rename(columns={"hpg_store_id": "store_id", "hpg_genre_name": "genre_name", "hpg_area_name": "area_name", "hpg_latitude": "latitude", "hpg_longitude": "longitude"})

import geopy.distance
from geopy import Point

store_relation["air_point"] = store_relation.apply(lambda row: Point(latitude=row['air_latitude'], longitude=row['air_longitude']), axis=1)
store_relation["hpg_point"] = store_relation.apply(lambda row: Point(latitude=row['hpg_latitude'], longitude=row['hpg_longitude']), axis=1)
store_relation["distance"] = store_relation["air_latitude"]

for i in range(store_relation.shape[0]):
    store_relation.loc[i, "distance"] = geopy.distance.distance(store_relation.loc[i, "air_point"], store_relation.loc[i, "hpg_point"]).km

store_relation = store_relation.sort_values("distance").reset_index(drop=True)
store_relation.tail()

import plotly.express as px

fig = px.histogram(store_relation, x='distance')
fig.show()

def get_delta(df):
    temp = deepcopy(df)
    
    temp["delta"] = temp["visit_datetime"] - temp["reserve_datetime"]
    temp = temp.rename(columns={"visit_datetime": "date"})
    temp['date'] = temp['date'].dt.date
    temp = temp.drop(columns=["reserve_datetime", "store_id"])

    def delta_days(x):
        if x.days < 1:
            return "<1"
        elif x.days <= 7:
            return "1 - 7"
        else:
            return ">7"


    temp["delta_days"] = temp["delta"].apply(delta_days)
    temp = temp.groupby(by=["date", "delta_days"]).reserve_visitors.agg(["sum", "count"]).reset_index().sort_values("date")

    fig = px.line(temp, x="date", y="count", color='delta_days')
    fig.show()
    fig = px.line(temp, x="date", y="sum", color='delta_days')
    fig.show()

get_delta(air_reserve)

get_delta(reserve_data)

import plotly.express as px

temp = deepcopy(visit_data)

temp["store_id"] = temp["store_id"].str[:3]
temp = temp.groupby(by=["date", "store_id"]).sum().reset_index()

fig = px.line(temp, x="date", y="visitors", color='store_id')
fig.show()

import plotly.express as px

temp = deepcopy(data)
temp["store_id"] = temp["store_id"].str[:3]

temp1 = deepcopy(temp)
temp1["store_id"] = "all"

temp = pd.concat([temp, temp1])

temp = temp.groupby(by=["date", "store_id"]).sum().reset_index()

fig = px.line(temp, x="date", y="visitors", color='store_id')
fig.show()

len(visit_data['store_id'].unique())

14154

len(data['store_id'].unique())

5519

temp = deepcopy(reserve_data)
    
temp["delta"] = temp["visit_datetime"] - temp["reserve_datetime"]
temp = temp.rename(columns={"visit_datetime": "date"})
temp['date'] = temp['date'].dt.date
temp = temp.drop(columns=["reserve_datetime"])
temp = temp.groupby(by=["store_id", "date"]).sum().reset_index()
temp["date"] = pd.to_datetime(temp["date"])
data = pd.merge(data, temp, on=["store_id", "date"])

data

def samples(df):
    ids = random.choices(data.where(data["store_id"].str[:3] == "air")["store_id"].unique(), k=3)

    for id in ids:
        temp = df.where(df["store_id"] == id).dropna().reset_index(drop=True)
        temp.sort_values("date")

        fig = go.Figure()
        fig.add_trace(go.Scatter(x=temp["date"], y=temp["visitors"],
                    mode='lines',
                    name='visitors'))
        fig.add_trace(go.Scatter(x=temp["date"], y=temp["reserve_visitors"],
                    mode='lines',
                    name='reserve_visitors'))
        fig.update_layout(title=id,
                   xaxis_title='date')

        fig.show()
        print(id)

samples(data)

air_63a750d8b4b6a976

air_d07e57b21109304a

air_3e93f3c81008696d

temp = deepcopy(data)
temp["month"] = [date.month_name() for date in temp["date"]]
temp = temp.drop(columns=["store_id", "date", "holiday_flg", "genre_name", "area_name", "latitude", "longitude", "day_of_week"])
temp = temp.groupby('month').mean().reset_index().sort_values("visitors")

fig = px.bar(temp, x='month', y='visitors',
             color='visitors', height=400)
fig.show()

fig = px.bar(data.groupby("holiday_flg")["visitors"].mean().reset_index().sort_values("visitors"), x='holiday_flg', y='visitors',
             color='visitors', height=400)
fig.show()

def genre_area_stores(df):
    temp = deepcopy(df)
    temp = temp.groupby('genre_name').count().reset_index()
    temp = temp.drop(columns=["store_id", "latitude", "longitude"])
    temp = temp.rename(columns={"area_name": "stores"}).sort_values("stores")


    fig = px.bar(temp.tail(50), x='genre_name', y='stores',
                 color='stores', height=400)
    fig.show()

    temp = deepcopy(df)
    temp = temp.groupby('area_name').count().reset_index()
    temp = temp.drop(columns=["store_id", "latitude", "longitude"])
    temp = temp.rename(columns={"genre_name": "stores"}).sort_values("stores")


    fig = px.bar(temp.tail(50), x='area_name', y='stores',
                 color='stores', height=600)
    fig.show()

genre_area_stores(air_store_info)

genre_area_stores(hpg_store_info)

genre_area_stores(data)

def genre_area_visitors(df):
    temp = deepcopy(df.drop(columns=["store_id", "latitude", "longitude", "date", "day_of_week", "holiday_flg", "delta", "area_name", "reserve_visitors"]))
    temp = temp.groupby('genre_name').sum().reset_index().sort_values("visitors")


    fig = px.bar(temp.tail(50), x='genre_name', y='visitors',
                 color='visitors', height=400)
    fig.show()

    temp = deepcopy(df.drop(columns=["store_id", "latitude", "longitude", "date", "day_of_week", "holiday_flg", "delta", "genre_name", "reserve_visitors"]))
    temp = temp.groupby('area_name').sum().reset_index().sort_values("visitors")


    fig = px.bar(temp.tail(50), x='area_name', y='visitors',
                 color='visitors', height=600)
    fig.show()

genre_area_visitors(data)

# from shapely.geometry import Point
# import geopandas

# longitudes = data["longitude"].tolist()
# latitudes = data["latitude"].tolist()

# world = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))
# japan = world[world.name == 'Japan']
# fig,ax = plt.subplots(figsize = (10,12))
# japan.plot(ax = ax)

# geometry = [Point(xy) for xy in zip(longitudes,latitudes)]
# geo_df = geopandas.GeoDataFrame(geometry = geometry)

# g = geo_df.plot(ax = ax, markersize = 20, color = 'red',marker = '*',label = 'Delhi')
# plt.show()

profile = ProfileReport(data, title="Profiling Report")
profile

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View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.

	store_id	visit_datetime	reserve_datetime	reserve_visitors
0	air_877f79706adbfb06	2016-01-01 19:00:00	2016-01-01 16:00:00	1
1	air_db4b38ebe7a7ceff	2016-01-01 19:00:00	2016-01-01 19:00:00	3
2	air_db4b38ebe7a7ceff	2016-01-01 19:00:00	2016-01-01 19:00:00	6
3	air_877f79706adbfb06	2016-01-01 20:00:00	2016-01-01 16:00:00	2
4	air_db80363d35f10926	2016-01-01 20:00:00	2016-01-01 01:00:00	5
...	...	...	...	...
2000315	hpg_2b293477ea4642fb	2017-05-31 20:00:00	2017-04-19 07:00:00	2
2000316	hpg_f922aeca48ca5a29	2017-05-31 20:00:00	2017-03-19 00:00:00	3
2000317	hpg_e9151de687b93aa3	2017-05-31 21:00:00	2017-04-14 20:00:00	49
2000318	hpg_fae6c96189b4a122	2017-05-31 21:00:00	2017-04-19 17:00:00	9
2000319	hpg_0b70de808b55ad1e	2017-05-31 23:00:00	2017-04-21 13:00:00	6

	store_id	visit_date	visitors
0	air_ba937bf13d40fb24	2016-01-13	25
1	air_ba937bf13d40fb24	2016-01-14	32
2	air_ba937bf13d40fb24	2016-01-15	29
3	air_ba937bf13d40fb24	2016-01-16	22
4	air_ba937bf13d40fb24	2016-01-18	6
...	...	...	...
1711375	hpg_fffc097dce87af3e	2017-03-10	2
1711376	hpg_fffc097dce87af3e	2017-03-17	2
1711377	hpg_fffc097dce87af3e	2017-03-27	5
1711378	hpg_fffc097dce87af3e	2017-04-21	3
1711379	hpg_fffc097dce87af3e	2017-04-23	8

	store_id	genre_name	area_name	latitude	longitude
0	air_0f0cdeee6c9bf3d7	Italian/French	Hyōgo-ken Kōbe-shi Kumoidōri	34.695124	135.197853
1	air_7cc17a324ae5c7dc	Italian/French	Hyōgo-ken Kōbe-shi Kumoidōri	34.695124	135.197853
2	air_fee8dcf4d619598e	Italian/French	Hyōgo-ken Kōbe-shi Kumoidōri	34.695124	135.197853
3	air_a17f0778617c76e2	Italian/French	Hyōgo-ken Kōbe-shi Kumoidōri	34.695124	135.197853
4	air_83db5aff8f50478e	Italian/French	Tōkyō-to Minato-ku Shibakōen	35.658068	139.751599
...	...	...	...	...	...
4685	hpg_c6ed353a48c7c93e	Sichuan food	Tōkyō-to Chiyoda-ku None	35.695780	139.768453
4686	hpg_a58d2423360b2fb4	Sichuan food	Tōkyō-to Minato-ku Roppongi	35.666137	139.733978
4687	hpg_d2021704076cdd70	Shanghai food	Tōkyō-to Chiyoda-ku None	35.695780	139.768453
4688	hpg_602472570d65e219	Spain/Mediterranean cuisine	Tōkyō-to Toshima-ku None	35.730068	139.711742
4689	hpg_11801306ce8bfb4f	Udon/Soba	Osaka Prefecture Osaka None	34.701519	135.498859

	calendar_date	day_of_week	holiday_flg
0	2016-01-01	Friday	1
1	2016-01-02	Saturday	1
2	2016-01-03	Sunday	1
3	2016-01-04	Monday	0
4	2016-01-05	Tuesday	0
...	...	...	...
512	2017-05-27	Saturday	0
513	2017-05-28	Sunday	0
514	2017-05-29	Monday	0
515	2017-05-30	Tuesday	0
516	2017-05-31	Wednesday	0

	store_id	visit_date	visitors
0	air_ba937bf13d40fb24	2016-01-13	25
1	air_ba937bf13d40fb24	2016-01-14	32
2	air_ba937bf13d40fb24	2016-01-15	29
3	air_ba937bf13d40fb24	2016-01-16	22
4	air_ba937bf13d40fb24	2016-01-18	6
...	...	...	...
1711375	hpg_fffc097dce87af3e	2017-03-10	2
1711376	hpg_fffc097dce87af3e	2017-03-17	2
1711377	hpg_fffc097dce87af3e	2017-03-27	5
1711378	hpg_fffc097dce87af3e	2017-04-21	3
1711379	hpg_fffc097dce87af3e	2017-04-23	8

air_reserve¶

hpg_reserve¶

visit data¶

air_store_info¶

hpg_store_info¶

date_info¶

	air_store_id	hpg_store_id
0	air_63b13c56b7201bd9	hpg_4bc649e72e2a239a
1	air_a24bf50c3e90d583	hpg_c34b496d0305a809
2	air_c7f78b4f3cba33ff	hpg_cd8ae0d9bbd58ff9
3	air_947eb2cae4f3e8f2	hpg_de24ea49dc25d6b8
4	air_965b2e0cf4119003	hpg_653238a84804d8e7
...	...	...
145	air_b8925441167c3152	hpg_0cc0abae3a8b52de
146	air_f8233ad00755c35c	hpg_098e4dd30e54fee6
147	air_6607fe3671242ce3	hpg_1004356dc8ab69ec
148	air_ccd19a5bc5573ae5	hpg_df22bf146d2c7e19
149	air_465bddfed3353b23	hpg_23c5945dc1a72454

	air_store_id	hpg_store_id	air_genre_name	air_area_name	air_latitude	air_longitude	hpg_genre_name	hpg_area_name	hpg_latitude	hpg_longitude	air_point	hpg_point	distance
58	air_d00161e19f08290b	hpg_05fada27f04e4383	Dining bar	Tōkyō-to Shinjuku-ku Kabukichō	35.693840	139.703549	International cuisine	None None None	35.688589	139.731971	35 41m 37.8244s N, 139 42m 12.7778s E	35 41m 18.9216s N, 139 43m 55.0955s E	2.637691
59	air_c1ff20617c54fee7	hpg_4be4a5cb851e45af	Izakaya	Tōkyō-to Meguro-ku Kamimeguro	35.641463	139.698171	International cuisine	Tōkyō-to Minato-ku Takanawa	35.637183	139.737998	35 38m 29.2664s N, 139 41m 53.4163s E	35 38m 13.8586s N, 139 44m 16.7929s E	3.638336
60	air_258ad2619d7bff9a	hpg_e87d00be48cd91c6	Izakaya	Tōkyō-to Kōtō-ku Tōyō	35.672854	139.817410	Japanese style	Tōkyō-to Sumida-ku Tachibana	35.704960	139.828642	35 40m 22.2726s N, 139 49m 2.67492s E	35 42m 17.8562s N, 139 49m 43.1123s E	3.704582
61	air_48f4da6223571da4	hpg_832ba309e6699258	Italian/French	Tōkyō-to Tachikawa-shi Izumichō	35.714014	139.407843	Italian	Tōkyō-to Hachiōji-shi Ishikawamachi	35.677207	139.373870	35 42m 50.4511s N, 139 24m 28.2352s E	35 40m 37.9442s N, 139 22m 25.9306s E	5.112082
62	air_640cf4835f0d9ba3	hpg_e63863c8987bd064	Izakaya	Tōkyō-to Ōta-ku Kamata	35.561257	139.716051	Japanese style	Kanagawa-ken Yokohama-shi Nagatsutachō	35.512762	139.495733	35 33m 40.5245s N, 139 42m 57.784s E	35 30m 45.9439s N, 139 29m 44.6382s E	20.691947

	store_id	date	visitors	day_of_week	holiday_flg	genre_name	area_name	latitude	longitude	reserve_visitors	delta
0	air_35512c42db0868da	2016-02-27	6	Saturday	0	Dining bar	Tōkyō-to Musashino-shi Midorichō	35.717784	139.566260	12	0 days 01:00:00
1	air_ee3a01f0c71a769f	2016-01-04	61	Monday	0	Cafe/Sweets	Shizuoka-ken Hamamatsu-shi Motoshirochō	34.710895	137.725940	2	0 days 04:00:00
2	air_ee3a01f0c71a769f	2016-01-08	21	Friday	0	Cafe/Sweets	Shizuoka-ken Hamamatsu-shi Motoshirochō	34.710895	137.725940	3	2 days 05:00:00
3	air_ee3a01f0c71a769f	2016-01-09	57	Saturday	0	Cafe/Sweets	Shizuoka-ken Hamamatsu-shi Motoshirochō	34.710895	137.725940	25	34 days 13:00:00
4	air_ee3a01f0c71a769f	2016-01-10	32	Sunday	0	Cafe/Sweets	Shizuoka-ken Hamamatsu-shi Motoshirochō	34.710895	137.725940	5	4 days 09:00:00
...	...	...	...	...	...	...	...	...	...	...	...
758042	hpg_ffe96ca8c6ec10cf	2017-03-31	8	Friday	0	Italian	Tōkyō-to Chūō-ku None	35.682504	139.773619	8	29 days 03:00:00
758043	hpg_ffe96ca8c6ec10cf	2017-04-05	4	Wednesday	0	Italian	Tōkyō-to Chūō-ku None	35.682504	139.773619	4	13 days 10:00:00
758044	hpg_ffe96ca8c6ec10cf	2017-04-07	11	Friday	0	Italian	Tōkyō-to Chūō-ku None	35.682504	139.773619	11	6 days 21:00:00
758045	hpg_ffe96ca8c6ec10cf	2017-04-15	3	Saturday	0	Italian	Tōkyō-to Chūō-ku None	35.682504	139.773619	3	40 days 06:00:00
758046	hpg_ffe96ca8c6ec10cf	2017-04-24	16	Monday	0	Italian	Tōkyō-to Chūō-ku None	35.682504	139.773619	16	5 days 22:00:00